Biography
Mohannad Abu Issa is a Postdoctoral Fellow in the Department of Systems and Computer Engineering at 杏吧原创 University. He received his Bachelor鈥檚 Degree in Communication and Electronics Engineering from the Jordan University of Science and Technology in 2011, his Master鈥檚 Degree in Communication Engineering from the University of Jordan in 2018, and his PhD in Electrical and Computer Engineering from 杏吧原创 University in 2026.
His research interests include artificial intelligence, machine learning, cybersecurity, edge and fog computing for the Internet of Things (IoT), federated learning, intrusion detection systems, and resource management for secure IoT and smart infrastructure deployments. His academic and industry experience spans AI-driven cybersecurity, IoT security, wireless communications, computer networking, engineering systems, and applied research collaboration with government and industry partners. He has published peer-reviewed papers in intrusion detection, IoT security, e-health IoT systems, federated learning, wireless communications, and cognitive radio networks.
His teaching experience includes serving as a Teaching Assistant in the Department of Systems and Computer Engineering at 杏吧原创 University, developing course materials and mini-projects for engineering courses in AI, computer networks, and network and software security, and serving as a Guest Lecturer for AI for Engineering topics related to deep neural networks, large language models, and AI ethics. He has also supervised and guided undergraduate and master鈥檚 students toward publishable research outputs, including IEEE conference publications and manuscripts currently under review.
Selected Publications
M. Abu Issa, M. Ibnkahla, A. Matrawy, and A. Eldosouky, 鈥淢ulti-Temporal Device Clustering for Federated Learning-Internet of Things Intrusion Detection Systems,鈥� IEEE Transactions on Machine Learning in Communications and Networking, under final review, 2026.
M. Abu Issa, L. Elian, B. Aboushaer, I. Rasool,聽and M. Ibnkahla, 鈥淓xplainable AI and Federated Learning-Based Intrusion Detection for IoT E-Health Systems,鈥� IEEE International Conference on Machine Learning in Communications and Networking, 2026.
M. Abu Issa, M. Ibnkahla, A. Matrawy, and A. Eldosouky, 鈥淭emporal Partitioned Federated Learning for IoT Intrusion Detection Systems,鈥� 2024 IEEE Wireless Communications and Networking Conference (WCNC), Dubai, United Arab Emirates, pp. 1鈥�6, 2024.
M. Abu Issa, A. Eldosouky, M. Ibnkahla, J. Jaskolka, and A. Matrawy, 鈥淚ntegrating Medical and Wearable Devices with E-Health Systems Using Horizontal IoT Platforms,鈥� 2023 IEEE Sensors Applications Symposium (SAS), Ottawa, ON, Canada, pp. 1鈥�6, 2023.
R. T. Al-Zubi, M. T. Abu Issa, A. A. Zghoul, K. A. Darabkh, and Y. Khattabi, 鈥淎nalysis of System Outage Probability in Underlay Cognitive Two-Way Amplify-and-Forward Relay Networks,鈥� Computer Communications, vol. 160, pp. 253鈥�262, July 2020.
R. T. Al-Zubi, M. T. Abu Issa, O. Jebreil, K. A. Darabkh, and Y. Khattabi, 鈥淥utage Performance of Cognitive Two-Way Amplify-and-Forward Relay Network under Different Transmission Schemes,鈥� Transactions on Emerging Telecommunications Technologies, vol. 31, no. 8, August 2020.